How much do Europeans know about the link between alcohol use and cancer? Results from an online survey in 14 countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Objective In the EU, which has the highest drinking levels worldwide, cancer is the primary cause of alcohol-attributable deaths. Existing studies show gaps in public knowledge, but there is lack of systematic appraisal. The report presents original data from a cross-sectional survey conducted within the framework of an online experimental study in 14 European countries, which among other things assessed baseline knowledge of the alcohol-NCD link, particularly cancer. Methods Online questionnaire among adults who consume alcohol conducted in 14 countries in 2022–2023 using different recruitment strategies and applying population weights for the final sample. Baseline assessments measured participants’ knowledge of alcohol-attributable health issues (with a specific focus on cancer). Results Baseline knowledge assessment showed that 90% indicated a causal role of alcohol for liver disease, 68% for heart diseases, and only 53% for cancer. Knowledge of specific alcohol-attributable cancer types was lower, with 39% aware of the link between alcohol use and colon cancer, 28% regarding oral cancer, and only 15% regarding female breast cancer. Knowledge levels varied across different countries and population groups. Conclusion Most Europeans do not know which cancers can be caused by alcohol use and knowledge is low specifically for female breast cancer. More awareness raising and prevention efforts are needed, such as the placement of cancer-specific health warnings on alcohol container labels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it